SpectralSlopeExtractor#

Defined in: voxatlas.features.acoustic.spectral.slope

class voxatlas.features.acoustic.spectral.slope.SpectralSlopeExtractor[source]#

Bases: BaseExtractor

Extract the acoustic.spectral.slope feature within the VoxAtlas pipeline.

This public extractor defines the reusable API for computing acoustic.spectral.slope from VoxAtlas structured inputs. It consumes None units and produces values aligned to frame units, making the extractor a stable pipeline node that can be cited independently of the surrounding execution machinery.

Algorithm#

The extractor follows the standard VoxAtlas feature-computation pattern.

  1. Input preparation Structured audio, unit tables, and dependency outputs are gathered from feature_input.

  2. Feature-specific computation The implementation applies the domain-specific transformation required by this extractor.

  3. Packaging Results are aligned to frame units and returned as a FeatureOutput object.

Notes

This extractor declares the upstream dependencies [‘acoustic.spectral.spectrum’] and is executed only after those features are available in the pipeline feature store.

Examples

>>> import numpy as np
>>> from voxatlas.features.acoustic.spectral.slope import SpectralSlopeExtractor
>>> from voxatlas.features.feature_input import FeatureInput
>>> from voxatlas.features.feature_output import MatrixFeatureOutput
>>> from voxatlas.pipeline.feature_store import FeatureStore
>>> store = FeatureStore()
>>> spectrum = MatrixFeatureOutput(
...     feature="acoustic.spectral.spectrum",
...     unit="frame",
...     time=np.array([0.0, 0.01], dtype=np.float32),
...     frequency=np.array([0.0, 1000.0, 2000.0], dtype=np.float32),
...     values=np.array([[1.0, 2.0, 3.0], [3.0, 2.0, 1.0]], dtype=np.float32),
... )
>>> store.add("acoustic.spectral.spectrum", spectrum)
>>> feature_input = FeatureInput(audio=None, units=None, context={"feature_store": store})
>>> out = SpectralSlopeExtractor().compute(feature_input, {})
>>> out.values.shape
(2,)
>>> float(np.sign(out.values[0]))
1.0
>>> float(np.sign(out.values[1]))
-1.0
name: str = 'acoustic.spectral.slope'#
input_units: str | None = None#
output_units: str | None = 'frame'#
dependencies: list[str] = ['acoustic.spectral.spectrum']#
default_config: dict = {}#
compute(feature_input, params)[source]#

Compute the extractor output for a single pipeline invocation.

This method is the reusable execution entry point for the extractor. It receives the standard FeatureInput bundle, applies the configured algorithm, and returns feature values aligned to the extractor output units for storage in the pipeline feature store.

Parameters:

  • feature_input (object) – Structured extractor input bundling audio, hierarchical units, and execution context for this feature computation.

  • params (object) – Resolved feature configuration for this invocation. Keys are feature-specific and merged from defaults and pipeline settings.

Returns:

Structured output aligned to the frame unit level when applicable.

Return type:

FeatureOutput

Examples

>>> import numpy as np
>>> from voxatlas.features.acoustic.spectral.slope import SpectralSlopeExtractor
>>> from voxatlas.features.feature_input import FeatureInput
>>> from voxatlas.features.feature_output import MatrixFeatureOutput
>>> from voxatlas.pipeline.feature_store import FeatureStore
>>> store = FeatureStore()
>>> spectrum = MatrixFeatureOutput(
...     feature="acoustic.spectral.spectrum",
...     unit="frame",
...     time=np.array([0.0], dtype=np.float32),
...     frequency=np.array([0.0, 1000.0, 2000.0], dtype=np.float32),
...     values=np.array([[1.0, 2.0, 3.0]], dtype=np.float32),
... )
>>> store.add("acoustic.spectral.spectrum", spectrum)
>>> feature_input = FeatureInput(audio=None, units=None, context={"feature_store": store})
>>> result = SpectralSlopeExtractor().compute(feature_input, {})
>>> result.unit
'frame'